[Association between anemia and 3-year all-cause mortality among oldest old people in longevity areas in China].
Bibliographic record
Abstract
OBJECTIVE: To explore the association between anemia and 3-year all-cause mortality among the oldest old people in longevity areas in China. METHODS: In August 2012, questionnaire survey, health examination and blood test were conducted among 929 old people aged ≥ 80 years in 7 longevity areas in China, who were included in Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2009. Cox regression model was used to evaluate the association between anemia or different hemoglobin levels and mortality. RESULTS: Among the 929 subjects, the prevalence of anemia was 49.6%, the main form of anemia was normocytic anemia. During the three year follow-up period, a total of 447 subjects died, the overall mortality was 49.8% (56.0% in subjects with anemia and 43.3% in subjects without anemia). Compared with the subjects without anemia, the mortality risk increased by 25% in the subjects with anemia after adjusting confounding factors (HR = 1.25, 95% CI: 1.03-1.52). Macrocytic anemia, simplex microcytic anemia and microcytic hypochromic anemia were all associated with the increased mortality in the oldest old people. Compared with the subjects with low hemoglobin concentration, the subjects with high hemoglobin concentration had a lower mortality risk, and the association was more obvious in women. CONCLUSION: Anemia and low hemoglobin concentration were associated with higher mortality risk in the oldest old people in China, indicating the importance of anemia prevention and treatment among this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".